{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:31:40Z","timestamp":1775104300832,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crowdsourced data from smart devices play an increasingly important role in water quality monitoring. However, guaranteeing and evaluating crowdsourced data quality is a key issue. This study aims to extract more accurate water reflectance data from smartphone photographs with variable exposure parameters, and to test the usability of these data in deriving water quality parameters. A set of low\u2013cost reference cards was designed to be placed in the center of the photograph near the water surface, and a calculation model was proposed to convert the photograph digital numbers (DNs) to water reflectance. A nonlinear DN\u2013to\u2013reflectance model was constructed using the inherent reflectance and DN of the reference card in the photograph. Then, the reflectance of the water surface in the same photograph was estimated. During the evaluation of this scheme in seven different waterbodies with 112 sampling sites, small differences were observed between the estimated and measured remote sensing reflectance; the average unbiased relative errors (AUREs) for the red, green, and blue bands were 25.7%, 29.5%, and 35.2%, respectively, while the RMSEs for the three bands were 0.0032, 0.0051, 0.0031, respectively. The derived water reflectance data were used to retrieve the Secchi\u2013disk depth (Zsd) and turbidity, with accuracies of 72.4% and 60.2%, respectively. The results demonstrate that the proposed method based on the smartphone camera can be used to derive the remote sensing reflectance and water quality parameters effectively with acceptable accuracy.<\/jats:p>","DOI":"10.3390\/rs14061371","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"1371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Smartphone\u2013Camera\u2013Based Water Reflectance Measurement and Typical Water Quality Parameter Inversion"],"prefix":"10.3390","volume":"14","author":[{"given":"Min","family":"Gao","sequence":"first","affiliation":[{"name":"School of Earth Science and Resources, China University of Geoscience, Beijing 100083, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8590-9736","authenticated-orcid":false,"given":"Junsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Shenglei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9628-1817","authenticated-orcid":false,"given":"Fangfang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}]},{"given":"Kai","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Ziyao","family":"Yin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Ya","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Earth Science and Resources, China University of Geoscience, Beijing 100083, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Wei","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Earth Science and Resources, China University of Geoscience, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Key Technologies and Systems of Surface Water Environment Monitoring by Remote Sensing","volume":"35","author":"Zhang","year":"2019","journal-title":"Environ. 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